The purpose of this paper is to offer an analytic perspective to policy makers and educational practitioners regarding how to use longitudinal achievement data to evaluate schools. The authors further discuss the potential practical applications of their models for superintendents, researchers, and policy makers. The premise of the study is that the complexity of the school context can be leveraged within longitudinal growth models to account for more variance than the unconditional counterparts of these models. The following research questions were considered: (1) Do school growth rates differ when school characteristics are taken into account in growth modeling?; (2) Do school growth rates differ when school initial status is taken into account in growth modeling?; and (3) How are schools evaluated differently based on the application of an unconditional model and a conditional model? When researchers and policy makers start to recommend the two-dimension matrix of initial score by rate of change and begin to evaluate schools based on it, this study suggests a three-dimension perspective that considers school contextual characteristics based on their initial status and rate of change. The study is also a demonstration of how schools can be evaluated under the context of a school accountability system in one state. This study can be used to provide a reference of school performance when an amount of growth for a particular school is compared to similar schools in a larger context. School districts, states, or educational funding organizations can use the conditional growth rates to evaluate schools based on the three-dimension matrix shown in this report. Appended are the following tables: (1) schools have different initial status; (2) schools have different percentages of FRL students; (3) schools have different percentages of Minority students; and (4) schools have different teacher student ratios. (Contains 5 figures, 5 tables and 2 footnotes.)